Building a common pediatric research terminology for accelerating child health research

The state and future of pediatric research-an introductory overview : The state and future of pediatric research series

IMPACT

This is an introduction to an article series devoted to the current state and future of pediatric research. The role of public-private partnerships, influencing factors, challenges, and recent trends in pediatric research are described, with emphasis on funding, drug and device development, physician-scientist training, and diversity. Potential solutions and advocacy opportunities are discussed.

Toward Better Understanding of Pediatric Feeding Disorder: A Proposed Framework for Patient Characterization

To establish a foundation for methodologically sound research on the epidemiology, assessment, and treatment of pediatric feeding disorder (PFD), a 28-member multidisciplinary panel with equal representation from medicine, nutrition, feeding skill, and psychology from seven national feeding programs convened to develop a case report form (CRF). This process relied upon recent advances in defining PFD, a review of the extant literature, expert consensus regarding best practices, and review of current patient characterization templates at participating institutions. The resultant PFD CRF involves patient characterization in four domains (ie, medical, nutrition, feeding skill, and psychosocial) and identifies the primary features of a feeding disorder based on PFD diagnostic criteria. A corresponding protocol provides guidance for completing the assessment process across the four domains. The PFD CRF promotes a standard procedure to support patient characterization, enhance methodological rigor, and provide a useful clinical tool for providers and researchers working with these disorders.

Pediatric Cardiopulmonary Exercise Testing: Interoperability Through Domain Analysis Modeling and a National Survey

PURPOSE

The electronic health record, data science advances, and dynamic environmental and infectious threats to child health highlight the need for harmonized and interoperable approaches to pediatric cardiopulmonary exercise testing (CPET). Accordingly, we developed a terminology harmonization in exercise medicine and exercise science domain analysis model (THEMES DAM) to structure CPET data elements.

METHODS

THEMES DAM identified 114 data elements, including participant information, calibration, equipment, protocols, laboratory personnel, encouragement strategies, and analysis procedures. We used the THEMES DAM, vetted by the international data standards organization HL7, to construct a current-state survey of pediatric CPET centers in the United States. Forty-eight of 101 centers responded to a questionnaire covering seven major topic areas (38 items).

RESULTS

Centers predominantly performed between 100 and 500 tests annually. Cardiac disease represented 55% of referrals. Almost all centers calibrated gas concentrations and flow daily, but 42% never calibrated their treadmill or cycle ergometers. All centers measured V̇O2peakbut calculated differently. Centers used a variety of protocols (e.g., for treadmill: 61%, Bruce; 43%, modified Bruce; 59%, other); 44% calculated CPET slopes from submaximal portions of CPET (e.g., V̇O2-HR). All centers verbally encouraged participants, but only 40% used a standardized approach. The interpretation of CPET was done by physicians (60%), exercise physiologists (25%), exercise technicians (10%), nurses (1%), or others (4%). Ninety-one percent would agree to collaborate in multicenter research, 89% to establish dynamic reference values, and 83% to better interpret CPET.

CONCLUSIONS

The survey data and the implementation of THEMES DAM could accelerate interoperability across multiple centers. This would facilitate a nimble approach to create pediatric reference values responsive to the constantly changing health environment and stimulate novel approaches to CPET research and clinical application.

Prerequisites to support high-quality clinical trials in children and young people

Children have the right to treatment based on the same quality of information that guides treatment in adults. Without the proper evaluation of medicinal products and devices in paediatric clinical trials that are designed to meet the rigorous standards of the competent authorities, children are discriminated from advances in medicine. There are regulatory, scientific and ethical incentives to address the knowledge gap regarding efficacy and safety of medicines in the paediatric population. High-quality clinical trials involving children of all ages can generate data that will ultimately close the knowledge gaps and support decision making.For clinical trials that enrol children, the needs are specialised and often resource intensive. Prerequisites for successful paediatric clinical trials are personnel with training in both paediatrics and neonatology and expertise in clinical trials in these populations. Moreover, national and international networks for efficient collaboration, dissemination of information, and sharing of resources and expertise are also needed, together with competent, efficient and high-quality local infrastructure with effective processes. Monitoring and oversight bodies with the relevant competence, including expertise in paediatrics, is also an important prerequisite for paediatric clinical trials. Compromise in any of these components will compromise the downstream results.This paper discusses the structures and competences needed in order to perform effective, high-quality paediatric clinical trials with the ultimate goal of better medicines and treatments for children. We propose a model of examining the process as a series of components that each has to be optimised, then all the components are actively optimised to function together as an ecosystem, and the resulting ecosystem functions well with the general research system and the healthcare delivery system.

Applied Methods for Estimating Transition Probabilities from Electronic Health Record Data

BACKGROUND

Electronic health record (EHR) data contain longitudinal patient information and standardized diagnostic codes. EHR data may be useful for estimating transition probabilities for state-transition models, but no guidelines exist on appropriate methods. We applied 3 potential methods to estimate transition probabilities from EHR data, using pediatric eating disorders (EDs) as a case study.

METHODS

We obtained EHR data from PEDsnet, which includes 8 US children's hospitals. Data included inpatient, outpatient, and emergency department visits for all patients with an ED. We mapped diagnoses to 3 ED health states: anorexia nervosa, bulimia nervosa, and other specified feeding or eating disorder. We estimated 1-y transition probabilities for males and females using 3 approaches: simple first-last proportions, a multistate Markov (MSM) model, and independent survival models.

RESULTS

Transition probability estimates varied widely between approaches. The first-last proportion approach estimated higher probabilities of remaining in the same health state, while the MSM and independent survival approaches estimated higher probabilities of transitioning to a different health state. All estimates differed substantially from published literature.

LIMITATIONS

As a source of health state information, EHR data are incomplete and sometimes inaccurate. EHR data were especially challenging for EDs, limiting the estimation and interpretation of transition probabilities.

CONCLUSIONS

The 3 approaches produced very different transition probability estimates. Estimates varied considerably from published literature and were rescaled and calibrated for use in a microsimulation model. Estimation of transition probabilities from EHR data may be more promising for diseases that are well documented in the EHR. Furthermore, clinicians and health systems should work to improve documentation of ED in the EHR. Further research is needed on methods for using EHR data to inform transition probabilities.

Inter-rater reliability of the neonatal adverse event severity scale using real-world Neonatal clinical trial data

OBJECTIVE

The Neonatal Adverse Event Severity Scale (NAESS) was developed to improve scoring of neonatal adverse events (AEs) and accelerate neonatal drug development. This is the first validation study of the novel tool.

STUDY DESIGN

Retrospective validation study assessing the inter-rater reliability (IRR) of the NAESS. Reviewers used real-world AE data from a neonatal trial. Intra-class correlation (ICC) statistical analysis was performed.

RESULT

Sixty AEs were randomly assigned to twelve reviewers for a total of 240 severity scores. Generic and AE-specific NAESS tables were assessed. The ICC was 0.63 (95% confidence interval 0.51 to 0.73). Percent variation due to reviewer and residual error was 0.03 and 0.34, respectively.

CONCLUSION

In this first study of the NAESS tool, an ICC of 0.63 indicates moderate reliability. Results highlight the need for improved data collection on neonatal AE forms, augmented training on the NAESS tool, and will inform the prospective validation studies.

Factors Influencing the Adoption of Health Information Standards in Health Care Organizations: A Systematic Review Based on Best Fit Framework Synthesis

Background

Since the early 1970s, health care provision has experienced rapid growth in the investment and adoption of health information technologies (HITs). However, the development and deployment of HITs has often been conducted in silos, at different organizational levels, within different regions, and in various health care settings; this has resulted in their infrastructures often being difficult to manage or integrate. Health information standards (ie, the set norms and requirements that underpin the deployment of HITs in health care settings) are expected to address these issues, yet their adoption remains to be frustratingly low among health care information technology vendors.

Objective

This study aimed to synthesize a comprehensive framework of factors that affect the adoption and deployment of health information standards by health care organizations.

Methods

First, electronic databases, including Web of Science, Scopus, and PubMed, were searched for relevant articles, with the results being exported to the EndNote reference management software. Second, study selection was conducted according to pre-established inclusion and exclusion criteria. Finally, a synthesized best fit framework was created, which integrated a thematic analysis of the included articles.

Results

In total, 35 records were incorporated into the synthesized framework, with 4 dimensions being identified: technology, organization, environment, and interorganizational relationships. The technology dimension included relative advantage, complexity, compatibility, trialability, observability, switching cost, standards uncertainty, and shared business process attributes. The organization dimension included organizational scale, organizational culture, staff resistance to change, staff training, top management support, and organizational readiness. The environment dimension included external pressure, external support, network externality, installed base, and information communication. Finally, the interorganizational relationships dimension included partner trust, partner dependence, relationship commitment, and partner power.

Conclusions

The synthesized framework presented in this paper extends the current understanding of the factors that influence the adoption of health information standards in health care organizations. It provides policy and decision makers with a greater awareness of factors that hinder or facilitate their adoption, enabling better judgement and development of adoption intervention strategies. Furthermore, suggestions for future research are provided.

An Analysis of the Learning Health System in Its First Decade in Practice: Scoping Review

Background

In the past decade, Lynn Etheredge presented a vision for the Learning Health System (LHS) as an opportunity for increasing the value of health care via rapid learning from data and immediate translation to practice and policy. An LHS is defined in the literature as a system that seeks to continuously generate and apply evidence, innovation, quality, and value in health care.

Objective

This review aimed to examine themes in the literature and rhetoric on the LHS in the past decade to understand efforts to realize the LHS in practice and to identify gaps and opportunities to continue to take the LHS forward.

Methods

We conducted a thematic analysis in 2018 to analyze progress and opportunities over time as compared with the initial Knowledge Gaps and Uncertainties proposed in 2007.

Results

We found that the literature on the LHS has increased over the past decade, with most articles focused on theory and implementation; articles have been increasingly concerned with policy.

Conclusions

There is a need for attention to understanding the ethical and social implications of the LHS and for exploring opportunities to ensure that these implications are salient in implementation, practice, and policy efforts.

Recommendations and Extraction of Clinical Variables of Pediatric Multiple Sclerosis Using Common Data Elements

PURPOSE

The purpose of this article was to demonstrate the feasibility of using common data elements (CDEs) to search for information on the pediatric patient with multiple sclerosis (MS) and provide recommendations for future quality improvement and research in the use of CDEs for pediatric MS symptom management strategies Methods: The St. Louis Children's Hospital (SLCH), Washington University (WU) pediatrics data network was evaluated for use of CDEs identified from a database to identify variables in pediatric MS, including the key clinical features from the disease course of MS. The algorithms used were based on International Classification of Diseases, Ninth/Tenth Revision, codes and text keywords to identify pediatric patients with MS from a de-identified database. Data from a coordinating center of SLCH/WU pediatrics data network, which houses inpatient and outpatient records consisting of patients (N = 498 000), were identified, and detailed information regarding the clinical course of MS were located from the text of the medical records, including medications, presence of oligoclonal bands, year of diagnosis, and diagnosis code.

RESULTS

There were 466 pediatric patients with MS, with a few also having the comorbid diagnosis of anxiety and depression.

CONCLUSIONS

St. Louis Children's Hospital/WU pediatrics data network is one of the largest databases in the United States of detailed data, with the ability to query and validate clinical data for research on MS. Nurses and other healthcare professionals working with pediatric MS patients will benefit from having common disease identifiers for quality improvement, research, and practice. The increased knowledge of big data from SLCH/WU pediatrics data network has the potential to provide information for intervention and decision-making that can be personalized to the pediatric MS patient.

What’s in a name? An overview of organisational health literacy terminology

Organisational health literacy (OHL) is a relatively new concept and its role in improving population health outcomes is gaining recognition. There are several terms being used in relation to OHL but there is no consensus about the definition of OHL nor agreement on a single approach to its application within health services. This contested space continues to create discussion and debate between health literacy researchers worldwide. Increasingly, health service accreditation standards are moving towards including OHL and so services need to clearly define their roles and responsibilities in this area. Inherent in this is the need to develop and validate quantifiable measures of OHL change. This is not to say it needs a ‘one-size-fits-all’ approach but rather that terminology needs to be fit for purpose. This paper reviews the literature on OHL, describing and contrasting OHL terminology to assist practitioners seeking OHL information and health services clarifying their roles and responsibilities in this area. What is known about the topic? Organisational health literacy (OHL) is a new and emerging field. Currently there is no agreed definition or approach to OHL. As a result there is a large number of terms being used to describe OHL and this can make it difficult for practitioners and health services to understand the meanings of the different terms and how they can be used when seeking OHL information and its application to health service policy. What does this paper add? This paper provides an overview of 19 different OHL terms currently in use and how they apply in a range of health service contexts. What are the implications for practitioners? This paper provides practitioners with an overview of OHL terms currently in use and how they can be used to seek information and evidence to inform practice or develop health service OHL policy. This will allow health services to ensure they can clearly define their roles and responsibilities in OHL for accreditation purposes by ensuring that terminology use is fit for purpose. Lastly, the paper provides an inventory of terminology to be used when searching for evidence-based practices in OHL. This ensures all relevant papers can be captured, leading to robust and thorough reviews of the evidence most relevant to the OHL area of focus.

Clinimetric properties of lower limb neurological impairment tests for children and young people with a neurological condition: A systematic review

Background

Clinicians and researchers require sound neurological tests to measure changes in neurological impairments necessary for clinical decision-making. Little evidence-based guidance exists for selecting and interpreting an appropriate, paediatric-specific lower limb neurological test aimed at the impairment level.

Objective

To determine the clinimetric evidence underpinning neurological impairment tests currently used in paediatric rehabilitation to evaluate muscle strength, tactile sensitivity, and deep tendon reflexes of the lower limb in children and young people with a neurological condition.

Methods

Thirteen databases were systematically searched in two phases, from the date of database inception to 16 February 2017. Lower limb neurological impairment tests were first identified which evaluated muscle strength, tactile sensitivity or deep tendon reflexes in children or young people under 18 years of age with a neurological condition. Papers containing clinimetric evidence of these tests were then identified. The methodological quality of each paper was critically appraised using standardised tools and clinimetric evidence synthesised for each test.

Results

Thirteen papers were identified, which provided clinimetric evidence on six neurological tests. Muscle strength tests had the greatest volume of clinimetric evidence, however this evidence focused on reliability. Studies were variable in quality with inconsistent results. Clinimetric evidence for tactile sensitivity impairment tests was conflicting and difficult to extrapolate. No clinimetric evidence was found for impairment tests of deep tendon reflexes.

Conclusions

Limited high-quality clinimetric evidence exists for lower limb neurological impairment tests in children and young people with a neurological condition. Results of currently used neurological tests, therefore, should be interpreted with caution. Robust clinimetric evidence on these tests is required for clinicians and researchers to effectively select and evaluate rehabilitation interventions.

Accelerating drug development for Alzheimer's disease through the use of data standards

INTRODUCTION

The exceedingly high rate of failed trials in Alzheimer's disease (AD) calls for immediate attention to improve efficiencies and learning from past, ongoing, and future trials. Accurate, highly rigorous standardized data are at the core of meaningful scientific research. Data standards allow for proper integration of clinical data sets and represent the essential foundation for regulatory endorsement of drug development tools. Such tools increase the potential for success and accuracy of trial results.

METHODS

The development of the Clinical Data Interchange Standards Consortium (CDISC) AD therapeutic area data standard was a comprehensive collaborative effort by CDISC and Coalition Against Major Diseases, a consortium of the Critical Path Institute. Clinical concepts for AD and mild cognitive impairment were defined and a data standards user guide was created from various sources of input, including data dictionaries used in AD clinical trials and observational studies.

RESULTS

A comprehensive collection of AD-specific clinical data standards consisting of clinical outcome measures, leading candidate genes, and cerebrospinal fluid and imaging biomarkers was developed. The AD version 2.0 (V2.0) Therapeutic Area User Guide was developed by diverse experts working with data scientists across multiple consortia through a comprehensive review and revision process. The AD CDISC standard is a publicly available resource to facilitate widespread use and implementation.

DISCUSSION

The AD CDISC V2.0 data standard serves as a platform to catalyze reproducible research, data integration, and efficiencies in clinical trials. It allows for the mapping and integration of available data and provides a foundation for future studies, data sharing, and long-term registries in AD. The availability of consensus data standards for AD has the potential to facilitate clinical trial initiation and increase sharing and aggregation of data across observational studies and among clinical trials, thereby improving our understanding of disease progression and treatment.

Big and disparate data: considerations for pediatric consortia

PURPOSE OF REVIEW

Increasingly, there is a need for examining exposure disease associations in large, diverse datasets to understand the complex determinants of pediatric disease and disability. Recognizing that children's health research consortia will be important sources of big data, it is crucial for the pediatric research community to be knowledgeable about the challenges and opportunities that they will face. The present review will provide examples of existing children's health consortia, highlight recent pooled analyses conducted by children's health research consortia, address common challenges of pooled analyses, and provide recommendations to advance collective research efforts in pediatric research.

RECENT FINDINGS

Formal consortia and other collective-science initiatives are increasingly being created to share individual data from a set of relevant epidemiological studies to address a common research topic under the concept that the joint effort of many individual groups can accomplish far more than working alone. There are practical challenges to the participation of investigators within consortia that need to be addressed in order for them to work.

SUMMARY

Researchers who access consortia with data centers will be able to go far beyond their initial hypotheses and potentially accomplish research that was previously thought infeasible or too costly.

Relating Complexity and Error Rates of Ontology Concepts. More Complex NCIt Concepts Have More Errors

OBJECTIVES

Ontologies are knowledge structures that lend support to many health-information systems. A study is carried out to assess the quality of ontological concepts based on a measure of their complexity. The results show a relation between complexity of concepts and error rates of concepts.

METHODS

A measure of lateral complexity defined as the number of exhibited role types is used to distinguish between more complex and simpler concepts. Using a framework called an area taxonomy, a kind of abstraction network that summarizes the structural organization of an ontology, concepts are divided into two groups along these lines. Various concepts from each group are then subjected to a two-phase QA analysis to uncover and verify errors and inconsistencies in their modeling. A hierarchy of the National Cancer Institute thesaurus (NCIt) is used as our test-bed. A hypothesis pertaining to the expected error rates of the complex and simple concepts is tested.

RESULTS

Our study was done on the NCIt's Biological Process hierarchy. Various errors, including missing roles, incorrect role targets, and incorrectly assigned roles, were discovered and verified in the two phases of our QA analysis. The overall findings confirmed our hypothesis by showing a statistically significant difference between the amounts of errors exhibited by more laterally complex concepts vis-à-vis simpler concepts.

CONCLUSIONS

QA is an essential part of any ontology's maintenance regimen. In this paper, we reported on the results of a QA study targeting two groups of ontology concepts distinguished by their level of complexity, defined in terms of the number of exhibited role types. The study was carried out on a major component of an important ontology, the NCIt. The findings suggest that more complex concepts tend to have a higher error rate than simpler concepts. These findings can be utilized to guide ongoing efforts in ontology QA.

Development of a Pediatric Adverse Events Terminology

In 2009, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Pediatric Terminology Harmonization Initiative to establish a core library of terms to facilitate the acquisition and sharing of knowledge between pediatric clinical research, practice, and safety reporting. A coalition of partners established a Pediatric Terminology Adverse Event Working Group in 2013 to develop a specific terminology relevant to international pediatric adverse event (AE) reporting. Pediatric specialists with backgrounds in clinical care, research, safety reporting, or informatics, supported by biomedical terminology experts from the National Cancer Institute's Enterprise Vocabulary Services participated. The multinational group developed a working definition of AEs and reviewed concepts (terms, synonyms, and definitions) from 16 pediatric clinical domains. The resulting AE terminology contains >1000 pediatric diseases, disorders, or clinical findings. The terms were tested for proof of concept use in 2 different settings: hospital readmissions and the NICU. The advantages of the AE terminology include ease of adoption due to integration with well-established and internationally accepted biomedical terminologies, a uniquely temporal focus on pediatric health and disease from conception through adolescence, and terms that could be used in both well- and underresourced environments. The AE terminology is available for use without restriction through the National Cancer Institute's Enterprise Vocabulary Services and is fully compatible with, and represented in, the Medical Dictionary for Regulatory Activities. The terminology is intended to mature with use, user feedback, and optimization.

What allows a health care system to become a learning health care system: Results from interviews with health system leaders

INTRODUCTION

The US health care system faces pressure to improve quality while managing complexity, curbing costs, and reducing inefficiency. These shortcomings have sparked interest in the Learning Health Care System (LHCS) as an alternate approach to organizing research and clinical care. Although diverse stakeholders have expressed support for moving toward an LHCS model, limited guidance exists for institutions considering such a transition.

METHODS

Interviews were conducted with institutional leaders from 25 health care systems considered to be at the forefront of LHCS. Interviews focused on the process of transitioning toward an LHCS, including motivations for change, key components, challenges encountered, and strategies for success, and on ethics and regulatory issues encountered. Qualitative analysis identified key themes across institutions.

RESULTS

Respondents described 5 themes related to the origin of their LHCS transformation: (1) visionary leadership or influence of a key individual, (2) adaptation to a changing health care landscape, (3) external funding, (4) regulatory or legislative influence, and (5) mergers or expansions. They described 6 challenges: (1) organizational culture, (2) data systems and data sharing, (3) funding learning activities, (4) limited supply of skilled individuals, (5) managing competing priorities, and (6) regulatory challenges. Finally, they suggested 8 strategies to support transformation: (1) strong leadership, (2) setting a limited number of organizational priorities, (3) building on existing strengths, (4) training programs, (5) "purposeful" design of data systems, (6) internal transparency of quality metrics, (7) payer/provider integration, and, within academic medical centers, (8) academic/clinical integration.

CONCLUSIONS

Even institutions at the forefront of LHCS described the transition as difficult. Their experiences provide insight into other institutions considering similar transitions, including elements essential for success and likely challenges.

Factors impacting the adoption decision of health data standards in tertiary healthcare organisations in Saudi Arabia

Purpose

Recent studies indicated that the level of adoption of health data standards in healthcare organisations remains frustratingly low worldwide although health data standards have been perceived to be an essential tool for interoperability barriers within health information systems. The relevant literature still lacks significant studies concerning the issues of the adoption process of health data standards in healthcare organisations, and in particular those in developing nation. In addressing this gap in knowledge, the purpose of this paper is to investigate the adoption decision of health data standards in tertiary healthcare organisations in Saudi Arabia, and to develop a technology-organisation-environment list that contains the critical factors influencing their adoption.

Design/methodology/approach

A multiple-case study methodology was conducted in Saudi Arabia and different data collection methods were used included semi-structured interviews with different decision makers at various levels and departments of the subject organisations, and documents analysis to identify critical factors to the adoption decision of health data standards.

Findings

The findings demonstrated a list of key factors from different aspects impacting the adoption decision of health data standards in the subject organisations. The technological factors are complexity and compatibility of health data standards, IT infrastructure, switching costs, market uncertainties, systems integration and enhancing the use of advanced systems. The main organisational factors are the lack of adequate policies and procedures and information management plan, resistance to change, data analysis and accreditation. The core environmental factors are the lack of national regulator and data exchange plan, national healthcare system and the shortage of professionals.

Research limitations/implications

The results from the qualitative data were difficult to generalise to other populations. For example, the structure of the health sector varies from country to country as each health sector has its own characteristics that affect and are affected by national circumstances. In order to provide a more grounded theory resulting from a qualitative study, further examination by conducting quantitative studies is required. In addition, the TOE approach does not take into account the sociotechnical issues and further research is required in this area.

Practical implications

The investigation into the adoption decision of health data standards in tertiary healthcare organisations in Saudi Arabia has led to the development of a technology-organisation-environment list that contains the critical factors influencing their adoption. The research outcome has addressed the gap in knowledge of the adoption of health data standards in healthcare organisations. It also provides the decision maker, and in particular those in developing nations, with better understanding of the adoption process of those standards to better judge and to develop suitable strategy of adoption interventions.

Originality/value

Although recent studies indicated that the level of adoption of health data standards in healthcare organisations remains frustratingly low, the prior studies related to health data standards missed out on the exploration of the adoption decision of different types of health data standards in healthcare organisations and the critical factors influencing their adoption. Research on health data standards adoption based out of a developing country such as Saudi Arabia can also potentially provide several new insights on standards practices.

PEDSnet: how a prototype pediatric learning health system is being expanded into a national network

Except for a few conditions, pediatric disorders are rare diseases. Because of this, no single institution has enough patients to generate adequate sample sizes to produce generalizable knowledge. Aggregating electronic clinical data from millions of children across many pediatric institutions holds the promise of producing sufficiently large data sets to accelerate knowledge discovery. However, without deliberately embedding these data in a pediatric learning health system (defined as a health care organization that is purposefully designed to produce research in routine care settings and implement evidence at the point of care), efforts to act on this new knowledge, reducing the distress and suffering that children experience when sick, will be ineffective. In this article we discuss a prototype pediatric learning health system, ImproveCareNow, for children with inflammatory bowel disease. This prototype is being scaled up to create PEDSnet, a national network that will support the efficient conduct of clinical trials, observational research, and quality improvement across diseases, specialties, and institutions.

Paediatric terminology in the Australian health and health-education context: a systematic review

AIM

This study aimed to identify paediatric terminology used in the Australian health and health-education context, propose a standardized framework for Australian use, and compare it with a US-based framework.

METHOD

Australian health and health-education websites were systematically searched using a novel hierarchical domain-specific search strategy to identify grey literature containing paediatric terminology. Webpages published from 2009 to February 2014, with a '.gov.au' or '.edu.au' domain and no advertising, were included. Paediatric terms were analysed with power-law distributions. Age definitions were grouped using a chi-squared test automatic interaction detection analysis (p<0.05).

RESULTS

In total, 34 paediatric terms and 197 unique age definitions were identified in 613 webpages. Terms displayed a language distribution, although definitions had semantic and lexical ambiguity. Age definitions were divided into four statistically different groups (F=245.3, p<0.001). Four paediatric terms with distinct age definitions were proposed based on Australian data: 'infant: 0 to <1 year', 'early childhood: 1 year to <5 years', 'child: 5 years to <13 years', and 'young person: 13 years to <22 years'. These recommendations were broader than the US-based comparison.

INTERPRETATION

This is a starting point for standardizing Australian paediatric terminology, and a method for exploring paediatric terminology in other countries.

Adverse drug reactions in neonates and infants: a population-tailored approach is needed

Drug therapy is a powerful tool to improve outcome, but there is an urgent need to improve pharmacotherapy in neonates through tailored prevention and management of adverse drug reactions (ADRs). At present, infants commonly receive off-label drugs, at dosages extrapolated from those in children or adults. Besides the lack of labelling, inappropriate formulations, (poly)pharmacy, immature organ function and multiple illnesses further raise the risk for ADRs in neonates and infants. Pharmacovigilance to improve the prevention and management of ADRs needs to be tailored to neonates and infants. We illustrate this using prevention strategies for drug prescription and administration errors (e.g. formulation, bedside manipulation, access), detection through laboratory signalling or clinical outlier data (e.g. reference laboratory values, overall high morbidity), assessment through algorithm scoring (e.g. Naranjo or population specific), as well as understanding of the developmental toxicology (e.g. covariates, developmental pharmacology) to avoid re-occurrence and for development of guidelines. Such tailored strategies need collaborative initiatives to combine the knowledge and expertise of different disciplines, but hold promise to become a very effective tool to improve pharmacotherapy and reduce ADRs in infants.

NIH/NCATS/GRDR® Common Data Elements: A leading force for standardized data collection

The main goal of the NIH/NCATS GRDR® program is to serve as a central web-based global data repository to integrate de-identified patient clinical data from rare disease registries, and other data sources, in a standardized manner, to be available to researchers for conducting various biomedical studies, including clinical trials and to support analyses within and across diseases. The aim of the program is to advance research for many rare diseases. One of the first tasks toward achieving this goal was the development of a set of Common Data Elements (CDEs), which are controlled terminologies that represent collected data. A list of 75 CDEs was developed by a national committee and was validated and implemented during a period of 2 year proof of concept. Access to GRDR CDEs is freely available at: https://grdr.ncats.nih.gov/index.php?option=com_content&view=article&id=3&Itemid=5. The GRDR CDEs have been the cornerstone of the GRDR repository, as well as of several other national and international patient registries. The establishment of the GRDR program has elevated the issue of data standardization and interoperability for rare disease patient registries, to international attention, resulting in a global dialog and significant change in the mindset of registry developers, patient advocacy groups, and other national and international organizations.

Addressing electronic clinical information in the construction of quality measures

Electronic health records (EHR) and registries play a central role in health care and provide access to detailed clinical information at the individual, institutional, and population level. Use of these data for clinical quality/performance improvement and cost management has been a focus of policy initiatives over the past decade. The Children's Health Insurance Program Reauthorization Act of 2009 (CHIPRA)-mandated Pediatric Quality Measurement Program supports development and testing of quality measures for children on the basis of electronic clinical information, including de novo measures and respecification of existing measures designed for other data sources. Drawing on the experience of Centers of Excellence, we review both structural and pragmatic considerations in e-measurement. The presence of primary observations in EHR-derived data make it possible to measure outcomes in ways that are difficult with administrative data alone. However, relevant information may be located in narrative text, making it difficult to interpret. EHR systems are collecting more discrete data, but the structure, semantics, and adoption of data elements vary across vendors and sites. EHR systems also differ in ability to incorporate pediatric concepts such as variable dosing and growth percentiles. This variability complicates quality measurement, as do limitations in established measure formats, such as the Quality Data Model, to e-measurement. Addressing these challenges will require investment by vendors, researchers, and clinicians alike in developing better pediatric content for standard terminologies and data models, encouraging wider adoption of technical standards that support reliable quality measurement, better harmonizing data collection with clinical work flow in EHRs, and better understanding the behavior and potential of e-measures.

The clinical translation gap in child health exercise research: a call for disruptive innovation

In children, levels of play, physical activity, and fitness are key indicators of health and disease and closely tied to optimal growth and development. Cardiopulmonary exercise testing (CPET) provides clinicians with biomarkers of disease and effectiveness of therapy, and researchers with novel insights into fundamental biological mechanisms reflecting an integrated physiological response that is hidden when the child is at rest. Yet the growth of clinical trials utilizing CPET in pediatrics remains stunted despite the current emphasis on preventative medicine and the growing recognition that therapies used in children should be clinically tested in children. There exists a translational gap between basic discovery and clinical application in this essential component of child health. To address this gap, the NIH provided funding through the Clinical and Translational Science Award (CTSA) program to convene a panel of experts. This report summarizes our major findings and outlines next steps necessary to enhance child health exercise medicine translational research. We present specific plans to bolster data interoperability, improve child health CPET reference values, stimulate formal training in exercise medicine for child health care professionals, and outline innovative approaches through which exercise medicine can become more accessible and advance therapeutics across the broad spectrum of child health.

PEDSnet: a National Pediatric Learning Health System

A learning health system (LHS) integrates research done in routine care settings, structured data capture during every encounter, and quality improvement processes to rapidly implement advances in new knowledge, all with active and meaningful patient participation. While disease-specific pediatric LHSs have shown tremendous impact on improved clinical outcomes, a national digital architecture to rapidly implement LHSs across multiple pediatric conditions does not exist. PEDSnet is a clinical data research network that provides the infrastructure to support a national pediatric LHS. A consortium consisting of PEDSnet, which includes eight academic medical centers, two existing disease-specific pediatric networks, and two national data partners form the initial partners in the National Pediatric Learning Health System (NPLHS). PEDSnet is implementing a flexible dual data architecture that incorporates two widely used data models and national terminology standards to support multi-institutional data integration, cohort discovery, and advanced analytics that enable rapid learning.